On the influence of noise on jump linear systems

Abstract

The plant mode will be assumed Perfectly measurable throughout the note and for the plant state, a noisy observation channel is introduced as M. MARITON dy=C(r(t))xdt+G(x(t), r(t))dw,+H(r(t))dos (3)Abs@acf-Jump linear systems are considered in a random environment where y E RP is the observation vector. Again matrices C, G , and H where they are subject to additive and multiplicative noises. Stochastic have appropriate dimensions and w4, u5 are independent Wiener processes notions of stabilizability and detectability are introduced to characterize in Rn4 and Rns which are also independent of wI, w 2 , and og. the asymptotic behavior of the optimal jump linear quadratic Gaussian Investigations are limited to the case where D, E, and G have the regulator and a dual Kalman fiiter. following form:

MixedH2/H∞pitch control of wind turbine with a Markovian jump model

“…Most work only concentrate on the static information of wind speed for choosing farm, or generate wind speed for testing. Meanwhile, Markovian jump system 2 has been well investigated due to the probabilistic description of model parameters switchings induced by external causes, e.g., random faults, unexpected events, uncontrolled configuration changes, see [26,27] and the references therein. However, up to date, there has been no related research work combining the stochastic property of wind speed into the control strategy of wind turbine, which is an interesting topic and leads to this study.…”

Time varying optimal control with packet losses

“…The LQG optimal control problem with missing observations can also be modelled using the well known Jump Linear System (JLS) theory [13], where the observer switches between open loop and closed loop configuration, depending on whether the packet containing the observation is lost, or arrives at the estimator in time. However, convergence results in this case can be obtained only when each jump sub-system is stabilizable and detectable.…”

A computational method for a class of jump linear quadratic systems

“…In [6], the optimal feedback control law depending only on measurable output rather than on the state of the system for the JLQ problem was established. This result was further improved when a JLQG regulator was designed in [5] where only part of the system state, the output, was measured.…”